Packet transport and load distribution in scale-free network models

Goh, K.-I.; Kahng, B.; Kim, D.

doi:10.1016/s0378-4371(02)01407-3

Cited by 29 publications

(12 citation statements)

References 27 publications

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“…A similar result was also confirmed by Goh et al [122]. In a related paper, Goh et al [123] investigated the relationship between degree correlation and centrality correlation.…”

Section: Node Centralitysupporting

confidence: 66%

Techniques for analyzing dynamic random graph models of web‐like networks: An overview

Cami

Deo

2007

Networks

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Various random graph models have recently been proposed to replicate and explain the topology of large, complex, real-life networks such as the World Wide Web and the Internet. These models are surveyed in this article. Our focus has primarily been on dynamic random graph models that attempt to account for the observed statistical properties of web-like networks through certain dynamic processes guided by simple stochastic rules. Particular attention is paid to the equivalence between mathematical definitions of dynamic random graphs in terms of inductively defined probability spaces and algorithmic definitions of such models in terms of recursive procedures. Several techniques that have been employed for studying dynamic random graphs-both heuristic and analytic-are expounded. Each technique is illustrated through its application in analyzing various graph parameters, such as degree distribution, degreecorrelation between adjacent nodes, clustering coefficient, distribution of node-pair distances, and connectedcomponent size. A discussion of the most recent salient work and a comprehensive list of references in this rapidly-expanding area are included.

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“…A similar result was also confirmed by Goh et al [122]. In a related paper, Goh et al [123] investigated the relationship between degree correlation and centrality correlation.…”

Section: Node Centralitysupporting

confidence: 66%

Techniques for analyzing dynamic random graph models of web‐like networks: An overview

Cami

Deo

2007

Networks

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“…The redistribution strategy is = / ∑ ∈Ω . The numeric value of betweenness centrality is proportional to that of degree with power exponent [2,15,16], so the definition of initial loads is substantially a nonlinear function of degree. Generally, we can conclude that initial loads are all defined as a function of degree.…”

Section: Introductionmentioning

confidence: 99%

A Novel Load Capacity Model with a Tunable Proportion of Load Redistribution against Cascading Failures

Zhang

Song

Xia

et al. 2018

Security and Communication Networks

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Defence against cascading failures is of great theoretical and practical significance. A novel load capacity model with a tunable proportion is proposed. We take degree and clustering coefficient into account to redistribute the loads of broken nodes. The redistribution is local, where the loads of broken nodes are allocated to their nearest neighbours. Our model has been applied on artificial networks as well as two real networks. Simulation results show that networks get more vulnerable and sensitive to intentional attacks along with the decrease of average degree. In addition, the critical threshold from collapse to intact states is affected by the tunable parameter. We can adjust the tunable parameter to get the optimal critical threshold and make the systems more robust against cascading failures.

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“…When the sum rule [29], i.e., P k g k d, is applied, one can see immediately that the diameter change distribution is the same as the total BC change distribution. On the other hand, the networks belonging to class II are more sparse and ramified than those in class I, so that the Internet is more fragile by the removal of a single vertex than the PIN.…”

mentioning

confidence: 94%

Probabilistic Prediction in Scale-Free Networks: Diameter Changes

Kim

Kahng

et al. 2003

Phys. Rev. Lett.

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In complex systems, responses to small perturbations are too diverse to definitely predict how much they would be, and then such diverse responses can be predicted in a probabilistic way. Here we study such a problem in scale-free networks, for example, the diameter changes by the deletion of a single vertex for various in silico and real-world scale-free networks. We find that the diameter changes are indeed diverse and their distribution exhibits an algebraic decay with an exponent zeta asymptotically. Interestingly, the exponent zeta is robust as zeta approximately 2.2(1) for most scale-free networks and insensitive to the degree exponents gamma as long as 2

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Packet transport and load distribution in scale-free network models

Cited by 29 publications

References 27 publications

Techniques for analyzing dynamic random graph models of web‐like networks: An overview

Techniques for analyzing dynamic random graph models of web‐like networks: An overview

A Novel Load Capacity Model with a Tunable Proportion of Load Redistribution against Cascading Failures

Probabilistic Prediction in Scale-Free Networks: Diameter Changes

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